Turn internal knowledge into training with evidence of performance

Convert approved material into courses, role tasks, open-ended evidence, reviewer feedback, and a maintainable programme. Define what a learner must decide, explain, demonstrate, or produce in the real role.

Convert approved material into courses, role tasks, open-ended evidence, reviewer feedback, and a maintainable programme. Use approved policies and material, record update ownership, and flag generated claims that need subject-matter review.

The connected steps are Source material → Course → Role task → Evidence → Human review → Update. Saved results remain editable in SubSchool. Tutoring homework generated from chat context is posted automatically when ready and can be edited afterward.

Define what a learner must decide, explain, demonstrate, or produce in the real role. Only use source material you are authorised to process, and remove unnecessary personal or sensitive information.

Use approved policies and material, record update ownership, and flag generated claims that need subject-matter review. When the current product workflow creates a saved course, lesson, exercise set, score, or feedback item, the teacher can inspect the supported fields instead of relying on a public-tool output as an automatic import.

Use scenarios, presentations, interviews, or written work with explicit criteria. AI can prepare or assess a supported first pass. The educator can inspect and change supported course, lesson, exercise, score, and feedback states; tutoring homework generated from authorised chat context is posted automatically and remains editable afterward.

Compare reviewer agreement, learner performance, preparation time, and operational load on one programme first. Course work can stay in the distinct draft state while it is reviewed. Private, public, and paid are separate course types rather than properties of the same draft.